From the 1 of 4 linked papers with an AI index.
4 papers
WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning
Haozhe Hu, Hao Wu, Peiran Yin +3
WIDE introduces a token-level dynamic width pruning framework for large language model inference, allowing each token to selectively activate attention heads and feed‑forward chann…
Beyond Single-Dimensional Compression: The Compound Sparsity Frontier of Large Language Models
Chao Han, Haozhe Hu, Xiaoyu Shen
Large language models (LLMs) are often compressed through static parameter pruning or dynamic token-level computation, yet aggressive sparsification can trigger rapid performance d…
UniRank: Unified Rank Allocation for Low-Rank LLM Compression
Chao Han, Haozhe Hu, Fei Ma +2
Low-rank decomposition serves as a promising compression paradigm for large language models, however, rank allocation remains challenging: manual rules lack generalizability, and l…
Informed Routing in LLMs: Smarter Token-Level Computation for Faster Inference
Chao Han, Yijuan Liang, Zihao Xuan +3
The deployment of large language models (LLMs) in real-world applications is increasingly limited by their high inference cost. While recent advances in dynamic token-level computa…